Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations834
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory97.7 KiB
Average record size in memory120.0 B

Variable types

Categorical2
Text3
Numeric9

Alerts

battery_type is highly overall correlated with brandHigh correlation
brand is highly overall correlated with battery_type and 1 other fieldsHigh correlation
discounted_price is highly overall correlated with memory and 2 other fieldsHigh correlation
display_size is highly overall correlated with brandHigh correlation
memory is highly overall correlated with discounted_price and 2 other fieldsHigh correlation
no of persons is highly overall correlated with reviewsHigh correlation
original_price is highly overall correlated with discounted_price and 2 other fieldsHigh correlation
reviews is highly overall correlated with no of personsHigh correlation
storage is highly overall correlated with discounted_price and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-08-14 11:00:43.914246
Analysis finished2025-08-14 11:00:52.315084
Duration8.4 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

brand
Categorical

High correlation 

Distinct19
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
XIAOMI
179 
REALME
144 
SAMSUNG
111 
INFINIX
71 
VIVO
63 
Other values (14)
266 

Length

Max length8
Median length7
Mean length5.8729017
Min length4

Characters and Unicode

Total characters4898
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowVIVO
2nd rowAPPLE
3rd rowVIVO
4th rowXIAOMI
5th rowXIAOMI

Common Values

ValueCountFrequency (%)
XIAOMI 179
21.5%
REALME 144
17.3%
SAMSUNG 111
13.3%
INFINIX 71
 
8.5%
VIVO 63
 
7.6%
APPLE 58
 
7.0%
OPPO 56
 
6.7%
MOTOROLA 53
 
6.4%
TECNO 28
 
3.4%
ITEL 18
 
2.2%
Other values (9) 53
 
6.4%

Length

2025-08-14T16:30:52.428420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xiaomi 179
21.4%
realme 144
17.2%
samsung 111
13.2%
infinix 71
 
8.5%
vivo 63
 
7.5%
apple 58
 
6.9%
oppo 56
 
6.7%
motorola 53
 
6.3%
tecno 28
 
3.3%
itel 18
 
2.1%
Other values (10) 57
 
6.8%

Most occurring characters

ValueCountFrequency (%)
I 685
14.0%
O 604
12.3%
A 570
11.6%
M 494
10.1%
E 405
8.3%
N 303
 
6.2%
L 300
 
6.1%
X 253
 
5.2%
P 237
 
4.8%
S 231
 
4.7%
Other values (11) 816
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 685
14.0%
O 604
12.3%
A 570
11.6%
M 494
10.1%
E 405
8.3%
N 303
 
6.2%
L 300
 
6.1%
X 253
 
5.2%
P 237
 
4.8%
S 231
 
4.7%
Other values (11) 816
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 685
14.0%
O 604
12.3%
A 570
11.6%
M 494
10.1%
E 405
8.3%
N 303
 
6.2%
L 300
 
6.1%
X 253
 
5.2%
P 237
 
4.8%
S 231
 
4.7%
Other values (11) 816
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 685
14.0%
O 604
12.3%
A 570
11.6%
M 494
10.1%
E 405
8.3%
N 303
 
6.2%
L 300
 
6.1%
X 253
 
5.2%
P 237
 
4.8%
S 231
 
4.7%
Other values (11) 816
16.7%

model
Text

Distinct304
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:52.643229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length26
Mean length15.517986
Min length7

Characters and Unicode

Total characters12942
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)11.4%

Sample

1st rowVIVO T1 44W
2nd rowAPPLE IPHONE 11
3rd rowVIVO T1 44W
4th rowPOCO M4 5G
5th rowREDMI 10
ValueCountFrequency (%)
5g 238
 
9.0%
realme 144
 
5.5%
pro 135
 
5.1%
samsung 111
 
4.2%
galaxy 111
 
4.2%
redmi 103
 
3.9%
note 73
 
2.8%
infinix 71
 
2.7%
vivo 63
 
2.4%
poco 62
 
2.4%
Other values (260) 1526
57.9%
2025-08-14T16:30:52.976871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2636
20.4%
O 977
 
7.5%
A 837
 
6.5%
E 800
 
6.2%
I 631
 
4.9%
P 603
 
4.7%
R 554
 
4.3%
G 539
 
4.2%
M 526
 
4.1%
N 521
 
4.0%
Other values (29) 4318
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2636
20.4%
O 977
 
7.5%
A 837
 
6.5%
E 800
 
6.2%
I 631
 
4.9%
P 603
 
4.7%
R 554
 
4.3%
G 539
 
4.2%
M 526
 
4.1%
N 521
 
4.0%
Other values (29) 4318
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2636
20.4%
O 977
 
7.5%
A 837
 
6.5%
E 800
 
6.2%
I 631
 
4.9%
P 603
 
4.7%
R 554
 
4.3%
G 539
 
4.2%
M 526
 
4.1%
N 521
 
4.0%
Other values (29) 4318
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2636
20.4%
O 977
 
7.5%
A 837
 
6.5%
E 800
 
6.2%
I 631
 
4.9%
P 603
 
4.7%
R 554
 
4.3%
G 539
 
4.2%
M 526
 
4.1%
N 521
 
4.0%
Other values (29) 4318
33.4%

colour
Text

Distinct375
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:53.250810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length16
Mean length10.610312
Min length1

Characters and Unicode

Total characters8849
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)21.9%

Sample

1st rowStarry Sky
2nd rowWhite
3rd rowMidnight Galaxy
4th rowPower Black
5th rowCaribbean Green
ValueCountFrequency (%)
black 230
 
15.1%
blue 201
 
13.2%
green 79
 
5.2%
white 33
 
2.2%
gold 32
 
2.1%
purple 30
 
2.0%
midnight 30
 
2.0%
grey 26
 
1.7%
silver 25
 
1.6%
yellow 17
 
1.1%
Other values (259) 822
53.9%
2025-08-14T16:30:53.619778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 930
 
10.5%
l 780
 
8.8%
711
 
8.0%
a 685
 
7.7%
r 546
 
6.2%
B 448
 
5.1%
i 396
 
4.5%
c 354
 
4.0%
n 342
 
3.9%
t 342
 
3.9%
Other values (41) 3315
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 930
 
10.5%
l 780
 
8.8%
711
 
8.0%
a 685
 
7.7%
r 546
 
6.2%
B 448
 
5.1%
i 396
 
4.5%
c 354
 
4.0%
n 342
 
3.9%
t 342
 
3.9%
Other values (41) 3315
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 930
 
10.5%
l 780
 
8.8%
711
 
8.0%
a 685
 
7.7%
r 546
 
6.2%
B 448
 
5.1%
i 396
 
4.5%
c 354
 
4.0%
n 342
 
3.9%
t 342
 
3.9%
Other values (41) 3315
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 930
 
10.5%
l 780
 
8.8%
711
 
8.0%
a 685
 
7.7%
r 546
 
6.2%
B 448
 
5.1%
i 396
 
4.5%
c 354
 
4.0%
n 342
 
3.9%
t 342
 
3.9%
Other values (41) 3315
37.5%

original_price
Real number (ℝ)

High correlation 

Distinct136
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26102.33
Minimum4899
Maximum159900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:53.694515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4899
5-th percentile8999
Q113999
median17999
Q325996.75
95-th percentile79931.5
Maximum159900
Range155001
Interquartile range (IQR)11997.75

Descriptive statistics

Standard deviation23800.797
Coefficient of variation (CV)0.91182654
Kurtosis9.1720005
Mean26102.33
Median Absolute Deviation (MAD)5000
Skewness2.9139157
Sum21769343
Variance5.6647794 × 108
MonotonicityNot monotonic
2025-08-14T16:30:53.811022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17999 96
 
11.5%
16999 40
 
4.8%
19999 39
 
4.7%
15999 27
 
3.2%
12999 27
 
3.2%
9999 25
 
3.0%
11999 25
 
3.0%
14999 25
 
3.0%
24999 22
 
2.6%
13999 21
 
2.5%
Other values (126) 487
58.4%
ValueCountFrequency (%)
4899 2
 
0.2%
5799 1
 
0.1%
6599 1
 
0.1%
6799 1
 
0.1%
6999 5
 
0.6%
7500 1
 
0.1%
7777 1
 
0.1%
7999 13
1.6%
8240 1
 
0.1%
8248 1
 
0.1%
ValueCountFrequency (%)
159900 1
 
0.1%
149999 1
 
0.1%
149900 1
 
0.1%
139900 6
0.7%
129900 3
0.4%
119900 3
0.4%
116999 2
 
0.2%
109900 1
 
0.1%
101999 1
 
0.1%
99900 7
0.8%

discounted_price
Real number (ℝ)

High correlation 

Distinct294
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21481.59
Minimum3599
Maximum152999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:54.024126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3599
5-th percentile6544.5
Q19999
median14999
Q321999
95-th percentile69999
Maximum152999
Range149400
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation21361.003
Coefficient of variation (CV)0.9943865
Kurtosis10.608068
Mean21481.59
Median Absolute Deviation (MAD)5500
Skewness3.0615992
Sum17915646
Variance4.5629245 × 108
MonotonicityNot monotonic
2025-08-14T16:30:54.189167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8999 26
 
3.1%
15999 25
 
3.0%
11999 23
 
2.8%
12999 22
 
2.6%
14999 21
 
2.5%
16999 21
 
2.5%
24999 20
 
2.4%
17999 19
 
2.3%
16499 16
 
1.9%
7499 15
 
1.8%
Other values (284) 626
75.1%
ValueCountFrequency (%)
3599 1
 
0.1%
4199 1
 
0.1%
4699 3
0.4%
4889 1
 
0.1%
5149 1
 
0.1%
5169 1
 
0.1%
5175 1
 
0.1%
5199 2
0.2%
5299 2
0.2%
5499 1
 
0.1%
ValueCountFrequency (%)
152999 1
 
0.1%
132999 2
0.2%
127999 4
0.5%
124999 1
 
0.1%
122999 4
0.5%
110999 3
0.4%
101999 1
 
0.1%
94999 2
0.2%
90999 4
0.5%
87999 3
0.4%

ratings
Real number (ℝ)

Distinct17
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2727818
Minimum0
Maximum4.7
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:54.282530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14.2
median4.3
Q34.4
95-th percentile4.635
Maximum4.7
Range4.7
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.28426554
Coefficient of variation (CV)0.066529385
Kurtosis122.427
Mean4.2727818
Median Absolute Deviation (MAD)0.1
Skewness-8.4609407
Sum3563.5
Variance0.080806898
MonotonicityNot monotonic
2025-08-14T16:30:54.387506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4.3 265
31.8%
4.2 197
23.6%
4.4 141
16.9%
4.1 65
 
7.8%
4.5 44
 
5.3%
4.7 42
 
5.0%
4 32
 
3.8%
4.6 18
 
2.2%
3.8 9
 
1.1%
3.9 9
 
1.1%
Other values (7) 12
 
1.4%
ValueCountFrequency (%)
0 2
 
0.2%
3 2
 
0.2%
3.3 1
 
0.1%
3.4 1
 
0.1%
3.5 2
 
0.2%
3.6 3
 
0.4%
3.7 1
 
0.1%
3.8 9
 
1.1%
3.9 9
 
1.1%
4 32
3.8%
ValueCountFrequency (%)
4.7 42
 
5.0%
4.6 18
 
2.2%
4.5 44
 
5.3%
4.4 141
16.9%
4.3 265
31.8%
4.2 197
23.6%
4.1 65
 
7.8%
4 32
 
3.8%
3.9 9
 
1.1%
3.8 9
 
1.1%

no of persons
Real number (ℝ)

High correlation 

Distinct397
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41788.124
Minimum0
Maximum1171704
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:54.483598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile65
Q1924
median5917.5
Q339955
95-th percentile187787
Maximum1171704
Range1171704
Interquartile range (IQR)39031

Descriptive statistics

Standard deviation90931.123
Coefficient of variation (CV)2.176004
Kurtosis45.451816
Mean41788.124
Median Absolute Deviation (MAD)5744
Skewness5.3397476
Sum34851295
Variance8.2684691 × 109
MonotonicityNot monotonic
2025-08-14T16:30:54.732333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4110 11
 
1.3%
16010 10
 
1.2%
190651 8
 
1.0%
186979 8
 
1.0%
738 7
 
0.8%
184191 7
 
0.8%
157763 6
 
0.7%
244 6
 
0.7%
3694 6
 
0.7%
122678 6
 
0.7%
Other values (387) 759
91.0%
ValueCountFrequency (%)
0 2
0.2%
3 2
0.2%
6 2
0.2%
10 2
0.2%
12 2
0.2%
13 2
0.2%
14 2
0.2%
17 1
0.1%
21 1
0.1%
25 1
0.1%
ValueCountFrequency (%)
1171704 1
0.1%
942649 1
0.1%
577272 2
0.2%
559353 1
0.1%
459210 1
0.1%
422214 1
0.1%
398759 2
0.2%
393417 1
0.1%
388968 1
0.1%
365540 2
0.2%

reviews
Real number (ℝ)

High correlation 

Distinct330
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3151.3705
Minimum0
Maximum122453
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:54.848800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q178.75
median514
Q33314
95-th percentile12739
Maximum122453
Range122453
Interquartile range (IQR)3235.25

Descriptive statistics

Standard deviation7532.4025
Coefficient of variation (CV)2.390199
Kurtosis89.020711
Mean3151.3705
Median Absolute Deviation (MAD)498
Skewness7.4607562
Sum2628243
Variance56737087
MonotonicityNot monotonic
2025-08-14T16:30:54.964258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 15
 
1.8%
63 11
 
1.3%
294 11
 
1.3%
3 10
 
1.2%
686 10
 
1.2%
21 10
 
1.2%
10468 8
 
1.0%
13531 8
 
1.0%
12739 8
 
1.0%
10818 7
 
0.8%
Other values (320) 736
88.2%
ValueCountFrequency (%)
0 5
 
0.6%
1 7
0.8%
2 15
1.8%
3 10
1.2%
4 3
 
0.4%
5 3
 
0.4%
6 3
 
0.4%
7 2
 
0.2%
8 6
 
0.7%
9 3
 
0.4%
ValueCountFrequency (%)
122453 1
 
0.1%
66962 1
 
0.1%
53824 2
0.2%
42174 1
 
0.1%
40463 1
 
0.1%
34039 1
 
0.1%
33908 2
0.2%
33202 3
0.4%
30022 1
 
0.1%
29491 1
 
0.1%

memory
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2973621
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:55.071941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9304392
Coefficient of variation (CV)0.36441519
Kurtosis-1.0326017
Mean5.2973621
Median Absolute Deviation (MAD)2
Skewness-0.073130991
Sum4418
Variance3.7265957
MonotonicityNot monotonic
2025-08-14T16:30:55.145941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 276
33.1%
4 230
27.6%
8 188
22.5%
2 72
 
8.6%
3 63
 
7.6%
1 5
 
0.6%
ValueCountFrequency (%)
1 5
 
0.6%
2 72
 
8.6%
3 63
 
7.6%
4 230
27.6%
6 276
33.1%
8 188
22.5%
ValueCountFrequency (%)
8 188
22.5%
6 276
33.1%
4 230
27.6%
3 63
 
7.6%
2 72
 
8.6%
1 5
 
0.6%

storage
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.78657
Minimum8
Maximum512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:55.213340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile32
Q164
median128
Q3128
95-th percentile256
Maximum512
Range504
Interquartile range (IQR)64

Descriptive statistics

Standard deviation68.636629
Coefficient of variation (CV)0.63092924
Kurtosis10.319772
Mean108.78657
Median Absolute Deviation (MAD)64
Skewness2.3733945
Sum90728
Variance4710.9868
MonotonicityNot monotonic
2025-08-14T16:30:55.280929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
128 411
49.3%
64 241
28.9%
32 105
 
12.6%
256 59
 
7.1%
512 8
 
1.0%
16 7
 
0.8%
8 3
 
0.4%
ValueCountFrequency (%)
8 3
 
0.4%
16 7
 
0.8%
32 105
 
12.6%
64 241
28.9%
128 411
49.3%
256 59
 
7.1%
512 8
 
1.0%
ValueCountFrequency (%)
512 8
 
1.0%
256 59
 
7.1%
128 411
49.3%
64 241
28.9%
32 105
 
12.6%
16 7
 
0.8%
8 3
 
0.4%
Distinct131
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:55.481038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length48
Median length43
Mean length20.559952
Min length6

Characters and Unicode

Total characters17147
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)4.2%

Sample

1st rowQualcomm Snapdragon 680
2nd rowA Bionic Chip
3rd rowQualcomm Snapdragon 680
4th rowMediatek Dimensity 700
5th rowQualcomm Snapdragon 680
ValueCountFrequency (%)
snapdragon 319
 
12.3%
qualcomm 313
 
12.1%
mediatek 299
 
11.5%
helio 202
 
7.8%
680 181
 
7.0%
dimensity 99
 
3.8%
unisoc 82
 
3.2%
core 71
 
2.7%
chip 58
 
2.2%
a 58
 
2.2%
Other values (109) 911
35.1%
2025-08-14T16:30:55.785707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1790
 
10.4%
a 1332
 
7.8%
o 1126
 
6.6%
e 1073
 
6.3%
i 1000
 
5.8%
n 972
 
5.7%
m 732
 
4.3%
d 631
 
3.7%
l 541
 
3.2%
c 506
 
3.0%
Other values (54) 7444
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1790
 
10.4%
a 1332
 
7.8%
o 1126
 
6.6%
e 1073
 
6.3%
i 1000
 
5.8%
n 972
 
5.7%
m 732
 
4.3%
d 631
 
3.7%
l 541
 
3.2%
c 506
 
3.0%
Other values (54) 7444
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1790
 
10.4%
a 1332
 
7.8%
o 1126
 
6.6%
e 1073
 
6.3%
i 1000
 
5.8%
n 972
 
5.7%
m 732
 
4.3%
d 631
 
3.7%
l 541
 
3.2%
c 506
 
3.0%
Other values (54) 7444
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1790
 
10.4%
a 1332
 
7.8%
o 1126
 
6.6%
e 1073
 
6.3%
i 1000
 
5.8%
n 972
 
5.7%
m 732
 
4.3%
d 631
 
3.7%
l 541
 
3.2%
c 506
 
3.0%
Other values (54) 7444
43.4%

display_size
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.524209
Minimum10.16
Maximum17.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:55.863704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.16
5-th percentile15.49
Q116.51
median16.66
Q316.76
95-th percentile17.22
Maximum17.78
Range7.62
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.68257046
Coefficient of variation (CV)0.041307301
Kurtosis20.088317
Mean16.524209
Median Absolute Deviation (MAD)0.15
Skewness-3.7307468
Sum13781.19
Variance0.46590243
MonotonicityNot monotonic
2025-08-14T16:30:55.995502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
16.76 146
17.5%
16.51 98
11.8%
17.02 64
 
7.7%
16.94 56
 
6.7%
15.49 45
 
5.4%
16.26 44
 
5.3%
16.71 39
 
4.7%
16.33 35
 
4.2%
16.56 33
 
4.0%
16.59 32
 
3.8%
Other values (32) 242
29.0%
ValueCountFrequency (%)
10.16 1
 
0.1%
12.7 8
 
1.0%
13.72 3
 
0.4%
13.84 10
 
1.2%
15.21 1
 
0.1%
15.44 1
 
0.1%
15.46 2
 
0.2%
15.49 45
5.4%
15.6 2
 
0.2%
15.7 1
 
0.1%
ValueCountFrequency (%)
17.78 1
 
0.1%
17.65 2
 
0.2%
17.53 3
 
0.4%
17.32 15
 
1.8%
17.27 9
 
1.1%
17.22 15
 
1.8%
17.09 3
 
0.4%
17.07 4
 
0.5%
17.02 64
7.7%
16.97 5
 
0.6%

battery_capacity
Real number (ℝ)

Distinct35
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4955.1859
Minimum1600
Maximum7000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2025-08-14T16:30:56.096714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1600
5-th percentile4151
Q15000
median5000
Q35000
95-th percentile6000
Maximum7000
Range5400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation484.71387
Coefficient of variation (CV)0.097819513
Kurtosis7.7897492
Mean4955.1859
Median Absolute Deviation (MAD)0
Skewness-0.86361277
Sum4132625
Variance234947.53
MonotonicityNot monotonic
2025-08-14T16:30:56.218338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
5000 618
74.1%
6000 63
 
7.6%
4500 41
 
4.9%
4000 17
 
2.0%
4700 12
 
1.4%
4230 10
 
1.2%
4250 7
 
0.8%
3000 7
 
0.8%
5020 6
 
0.7%
4980 5
 
0.6%
Other values (25) 48
 
5.8%
ValueCountFrequency (%)
1600 1
 
0.1%
2400 1
 
0.1%
3000 7
0.8%
3020 3
 
0.4%
3300 2
 
0.2%
3700 3
 
0.4%
3900 1
 
0.1%
4000 17
2.0%
4020 4
 
0.5%
4050 2
 
0.2%
ValueCountFrequency (%)
7000 3
 
0.4%
6000 63
 
7.6%
5200 1
 
0.1%
5160 1
 
0.1%
5080 2
 
0.2%
5065 1
 
0.1%
5050 1
 
0.1%
5020 6
 
0.7%
5000 618
74.1%
4980 5
 
0.6%

battery_type
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
Lithium
556 
Lithium Ion
164 
Lithium Polymer
114 

Length

Max length15
Median length7
Mean length8.8800959
Min length7

Characters and Unicode

Total characters7406
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLithium
2nd rowLithium
3rd rowLithium
4th rowLithium Polymer
5th rowLithium Polymer

Common Values

ValueCountFrequency (%)
Lithium 556
66.7%
Lithium Ion 164
 
19.7%
Lithium Polymer 114
 
13.7%

Length

2025-08-14T16:30:56.350935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T16:30:56.434393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lithium 834
75.0%
ion 164
 
14.7%
polymer 114
 
10.3%

Most occurring characters

ValueCountFrequency (%)
i 1668
22.5%
m 948
12.8%
L 834
11.3%
t 834
11.3%
h 834
11.3%
u 834
11.3%
278
 
3.8%
o 278
 
3.8%
I 164
 
2.2%
n 164
 
2.2%
Other values (5) 570
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1668
22.5%
m 948
12.8%
L 834
11.3%
t 834
11.3%
h 834
11.3%
u 834
11.3%
278
 
3.8%
o 278
 
3.8%
I 164
 
2.2%
n 164
 
2.2%
Other values (5) 570
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1668
22.5%
m 948
12.8%
L 834
11.3%
t 834
11.3%
h 834
11.3%
u 834
11.3%
278
 
3.8%
o 278
 
3.8%
I 164
 
2.2%
n 164
 
2.2%
Other values (5) 570
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1668
22.5%
m 948
12.8%
L 834
11.3%
t 834
11.3%
h 834
11.3%
u 834
11.3%
278
 
3.8%
o 278
 
3.8%
I 164
 
2.2%
n 164
 
2.2%
Other values (5) 570
 
7.7%

Interactions

2025-08-14T16:30:51.331251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:44.392294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:45.353110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.308643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.183028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.994434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.781108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.576861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.332622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.424026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:44.506119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:45.451038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.528729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.278699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.085015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.880829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.649240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.530787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.496369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:44.590132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:45.548645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.620677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.380997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.177708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.967133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.751491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.633414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.593435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:44.689360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:45.638939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.691333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.463731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.257426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.051486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.832119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.722844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.677391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:44.803755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:45.738674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.776016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.554826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.335965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.138831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.915203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.897023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.759506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:44.889379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:45.916664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.858510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.638575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.417856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.227461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.997961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.984843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.839309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:44.986932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.028567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.938904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.718027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.520425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.298886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.077507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.062609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.922522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:45.085193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.122334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.008642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.812136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.606385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.397021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.149829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.153526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.996798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:45.220215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:46.221516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.104651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:47.904937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:48.683743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:49.490270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:50.248138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-14T16:30:51.229964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-14T16:30:56.508550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
battery_capacitybattery_typebranddiscounted_pricedisplay_sizememoryno of personsoriginal_priceratingsreviewsstorage
battery_capacity1.0000.1520.349-0.2240.274-0.1960.125-0.2290.0100.101-0.095
battery_type0.1521.0000.5590.1460.1460.0930.1050.1490.0000.0620.095
brand0.3490.5591.0000.2840.5350.2950.1090.2920.4360.0190.312
discounted_price-0.2240.1460.2841.0000.0970.816-0.0510.9220.319-0.0040.828
display_size0.2740.1460.5350.0971.0000.169-0.1420.072-0.160-0.1130.189
memory-0.1960.0930.2950.8160.1691.000-0.0770.7820.098-0.0220.805
no of persons0.1250.1050.109-0.051-0.142-0.0771.000-0.0380.4150.991-0.072
original_price-0.2290.1490.2920.9220.0720.782-0.0381.0000.2490.0120.773
ratings0.0100.0000.4360.319-0.1600.0980.4150.2491.0000.3820.166
reviews0.1010.0620.019-0.004-0.113-0.0220.9910.0120.3821.000-0.027
storage-0.0950.0950.3120.8280.1890.805-0.0720.7730.166-0.0271.000

Missing values

2025-08-14T16:30:52.133990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-14T16:30:52.230485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

brandmodelcolouroriginal_pricediscounted_priceratingsno of personsreviewsmemorystorageprocessordisplay_sizebattery_capacitybattery_type
0VIVOVIVO T1 44WStarry Sky19990144994.58733160444.0128.0Qualcomm Snapdragon 68016.365000.0Lithium
1APPLEAPPLE IPHONE 11White48900471994.6184191108188.0128.0A Bionic Chip15.495000.0Lithium
2VIVOVIVO T1 44WMidnight Galaxy20990159994.45136537506.0128.0Qualcomm Snapdragon 68016.365000.0Lithium
3XIAOMIPOCO M4 5GPower Black15999119994.25344841854.064.0Mediatek Dimensity 70016.715000.0Lithium Polymer
4XIAOMIREDMI 10Caribbean Green1499992994.3187787120844.064.0Qualcomm Snapdragon 68017.026000.0Lithium Polymer
5XIAOMIPOCO M4 5GCool Blue15999119994.25344841854.064.0Mediatek Dimensity 70016.715000.0Lithium Polymer
6XIAOMIPOCO C31Shadow Gray1199974994.3202727116724.064.0MediaTek Helio G3516.595000.0Lithium
7XIAOMIPOCO M4 5GYellow15999119994.25344841854.064.0Mediatek Dimensity 70016.715000.0Lithium Polymer
8XIAOMIREDMI 10Midnight Black1499992994.3187787120844.064.0Qualcomm Snapdragon 68017.026000.0Lithium Polymer
9VIVOVIVO T1 44WMidnight Galaxy23990179994.357984838.0128.0Qualcomm Snapdragon 68016.365000.0Lithium
brandmodelcolouroriginal_pricediscounted_priceratingsno of personsreviewsmemorystorageprocessordisplay_sizebattery_capacitybattery_type
826XIAOMIXIAOMI 11T PRO 5G HYPERPHONECelestial Magic35899335004.1496568.0128.0Qualcomm Snapdragon 68016.945000.0Lithium
827XIAOMIREDMI NOTE 10 PRO MAXGlacial Blue22999203904.31683312716.0128.0Qualcomm Snapdragon 732G16.945020.0Lithium
828XIAOMIPOCO M2 PROOut of the Blue17999179994.4350744332026.064.0Qualcomm Snapdragon 720G16.945000.0Lithium
829TECNOTECNO SPARK 7Spruce Green899984904.21487892.032.0Helio A2516.566000.0Lithium
830XIAOMIPOCO X2Matrix Purple17999179994.5271179289966.064.0Qualcomm Snapdragon 730G16.944500.0Lithium
831REALMEREALME GT NEO 2NEO Blue38999359994.444306286.0256.0Qualcomm Snapdragon 87016.815000.0Lithium
832REALMEREALME GT NEO 2NEO Black38999359994.444306286.0256.0Qualcomm Snapdragon 87016.815000.0Lithium
833REALMEREALME GT NEO 2NEO Black34999319994.41819424788.0128.0Qualcomm Snapdragon 87016.815000.0Lithium
834REALMEREALME X50 PRORust Red17999419994.4868912328.0128.0Qualcomm Snapdragon 68016.364200.0Lithium
835XIAOMIPOCO M2 PROTwo Shades of Black17999179994.4350744332026.064.0Qualcomm Snapdragon 720G16.945000.0Lithium